{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy import signal\n",
    "import matplotlib.pyplot as plt\n",
    "from heartnet_v1 import heartnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "##initialize parameters of heartnet\n",
    "bn_momentum = 0.99\n",
    "random_seed = 1\n",
    "eps= 1.1e-5\n",
    "bias=False\n",
    "l2_reg=0.\n",
    "l2_reg_dense=0.\n",
    "kernel_size=5\n",
    "maxnorm=10000.\n",
    "dropout_rate=0.5\n",
    "dropout_rate_dense=0.\n",
    "padding='valid'\n",
    "activation_function='relu'\n",
    "subsam=2\n",
    "\n",
    "lr=0.0007\n",
    "lr_decay=1e-8\n",
    "\n",
    "#load_path='/media/taufiq/Data/heart_sound/models/fold0 2017-12-27 13:12:34.499110/weights.0022-0.7631.hdf5'\n",
    "# load_path='/media/taufiq/Data/heart_sound/models/fold2 2017-12-26 15:20:36.867116/weights.0187-0.8527.hdf5'\n",
    "#load_path='/media/taufiq/Data/heart_sound/remoteIICTweights/fold1 2017-12-20 15:26:47.011204/weights.0091-0.8898.hdf5'\n",
    "# load_path='/media/taufiq/Data/heart_sound/models/fold0_noFIR 2018-01-16 17:25:30.579232/weights.0193-0.7411.hdf5'\n",
    "# FIR non-learn\n",
    "# load_path='/media/taufiq/Data/heart_sound/models/fold1_noFIR 2018-01-13 16:05:20.862887/weights.0170-0.8719.hdf5'\n",
    "# FIR init learnable\n",
    "# load_path='/media/taufiq/Data/heart_sound/models/fold1_noFIR 2018-01-16 19:58:54.149714/weights.0182-0.8773.hdf5'\n",
    "# load_path='/media/taufiq/Data/heart_sound/models/fold2_noFIR 2018-01-16 13:40:22.284989/weights.0179-0.8756.hdf5'\n",
    "# load_path='/media/taufiq/Data/heart_sound/models/fold3_noFIR 2018-01-16 19:59:03.934614/weights.0183-0.8581.hdf5'\n",
    "# load_path='/media/taufiq/Data/heart_sound/models/fold2_noFIR 2018-01-21 11:57:12.236015/weights.0183-0.8311.hdf5'\n",
    "# Random init\n",
    "# load_path='/media/taufiq/Data/heart_sound/models/fold1_noFIR 2018-01-23 11:15:00.303289/weights.0037-0.8288.hdf5'\n",
    "# Uniform init\n",
    "# load_path='/media/taufiq/Data/heart_sound/models/fold1_noFIR 2018-01-23 12:27:31.167478/weights.0181-0.8754.hdf5'\n",
    "# Linear phase\n",
    "load_path='/media/taufiq/Data/heart_sound/models/fold1_noFIR 2018-01-24 16:28:06.346153/weights.0083-0.8925.hdf5'\n",
    "#LP-Rand\n",
    "# load_path='/media/taufiq/Data/heart_sound/models/fold2_noFIR 2018-02-02 18:19:58.579053/weights.0194-0.8750.hdf5'\n",
    "fs = 2000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create heartnet with load path specified\n",
    "\n",
    "model = heartnet(load_path,activation_function, bn_momentum=0.99, bias=False, dropout_rate=0.5, dropout_rate_dense=0.0,\n",
    "             eps=1.1e-5, kernel_size=5, l2_reg=0.0, l2_reg_dense=0.0,lr=0.0012843784, lr_decay=0.0001132885, maxnorm=10000.,\n",
    "             padding='valid', random_seed=1, subsam=2, num_filt=(8, 4), num_dense=20,FIR_train=False)\n",
    "# model.summary()\n",
    "weights = model.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 31 into shape (61,1)",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-08b7208a7cb6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0minput_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m \u001b[0;31m# 0-3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mweights_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mweights_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m61\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/taufiq/.local/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(a, newshape, order)\u001b[0m\n\u001b[1;32m    230\u001b[0m            [5, 6]])\n\u001b[1;32m    231\u001b[0m     \"\"\"\n\u001b[0;32m--> 232\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'reshape'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/taufiq/.local/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;31m# An AttributeError occurs if the object does not have\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 31 into shape (61,1)"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    input_ = i # 0-3\n",
    "    weights_ = np.hstack(weights[input_])\n",
    "    weights_ = np.reshape(weights_,[61,1])\n",
    "\n",
    "    for i in range(0,1):\n",
    "        w,freq_res=signal.freqz(weights_[:,i])\n",
    "        plt.plot(w/np.pi*500,10*np.log10(abs(freq_res)/max(abs(freq_res))))\n",
    "    plt.grid()\n",
    "    plt.xlabel('Frequency (Hz)')\n",
    "    plt.ylabel('Amplitude (dB)')\n",
    "    plt.xticks(range(0,501,50))\n",
    "#     plt.xscale('log')\n",
    "    plt.title('Filter of input branch ' + np.str(input_))\n",
    "#     plt.hold\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 31 into shape (61,1)",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-058e5b205b13>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0minput_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m \u001b[0;31m# 0-3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mweights_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mweights_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m61\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0max1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m111\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfreq_res\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreqz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/taufiq/.local/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(a, newshape, order)\u001b[0m\n\u001b[1;32m    230\u001b[0m            [5, 6]])\n\u001b[1;32m    231\u001b[0m     \"\"\"\n\u001b[0;32m--> 232\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'reshape'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/taufiq/.local/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;31m# An AttributeError occurs if the object does not have\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 31 into shape (61,1)"
     ],
     "output_type": "error"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc20d0add90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    fig = plt.figure()\n",
    "    input_ = i # 0-3\n",
    "    weights_ = np.hstack(weights[input_])\n",
    "    weights_ = np.reshape(weights_,[61,1])\n",
    "    ax1 = fig.add_subplot(111)\n",
    "    w,freq_res=signal.freqz(weights_)\n",
    "    \n",
    "    plt.plot(w/np.pi*500,10*np.log10(abs(freq_res)/max(abs(freq_res))))\n",
    "    plt.grid()\n",
    "    plt.xlabel('Frequency (Hz)')\n",
    "    plt.ylabel('Amplitude (dB)')\n",
    "    plt.xticks(range(0,501,50))\n",
    "    \n",
    "    angles = np.unwrap(np.angle(freq_res))\n",
    "    ax2 = ax1.twinx()\n",
    "    plt.plot(w/np.pi*500, angles, 'g')\n",
    "    plt.ylabel('Angle (radians)', color='g')\n",
    "#     plt.xscale('log')\n",
    "    plt.title('Filter of input branch ' + np.str(input_))\n",
    "#     plt.hold\n",
    "    plt.axis('tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 31 into shape (61,1)",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-0140c719d4ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0minput_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mi\u001b[0m \u001b[0;31m# 0-3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mweights_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mweights_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m61\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/taufiq/.local/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc\u001b[0m in \u001b[0;36mreshape\u001b[0;34m(a, newshape, order)\u001b[0m\n\u001b[1;32m    230\u001b[0m            [5, 6]])\n\u001b[1;32m    231\u001b[0m     \"\"\"\n\u001b[0;32m--> 232\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'reshape'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/taufiq/.local/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;31m# An AttributeError occurs if the object does not have\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 31 into shape (61,1)"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    input_ = i # 0-3\n",
    "    weights_ = np.hstack(weights[input_])\n",
    "    weights_ = np.reshape(weights_,[61,1])\n",
    "    plt.plot(weights_)\n",
    "    plt.xlim(0,60)\n",
    "    plt.yticks([0,0.08,0.04])\n",
    "#     plt.axis('off')\n",
    "    plt.tick_params(\n",
    "        axis='x',          # changes apply to the x-axis\n",
    "        which='both',      # both major and minor ticks are affected\n",
    "        labelsize=18.,\n",
    "        bottom='off',      # ticks along the bottom edge are off\n",
    "        top='off',         # ticks along the top edge are off\n",
    "        labelbottom='off') \n",
    "    plt.tick_params(\n",
    "        axis='y',          # changes apply to the x-axis\n",
    "        which='both',      # both major and minor ticks are affected\n",
    "        rotation=90,\n",
    "        labelsize=18.)\n",
    "#         left='off',      # ticks along the bottom edge are off\n",
    "#         right='off',         # ticks along the top edge are off\n",
    "#         labelleft='off') # labels along the bottom edge are off\n",
    "#     plt.title('Filter of input branch ' + np.str(input_))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc20d0b7310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ljhb817bD+OV7x3DNSiu+vC4/JJ0Lgx7I/S4PNv26Ah2OUawrSsZPrirBhvkpH3vytZgNsJgNmJ8WCwDYVJqJl/fZ8LO3GvC5Rypw3aoc3HvRAiQY1b0QNNfVtjuQbzEhPiZ0TxThVJIVj/1tXNhTO/e4D1sqWvDrvzfCL8v4xsZ5uH1D4WmnTS8uTsfFxemobXdgS0ULnqhsxTO7juPl29cG/eExqJMifr+Me1+qRe+QG3/+6mr86Surcd7C1DNOQ2g0Eq5ebsX2e8/FjWvy8NzuNpz38w+ws9EezOFRmNV3RMaCXkBZdgI6HKOwD7uVHgrNUKvdhc/8cgcefPsIzp5nwXvf3IBvbJw/pTWsMmsCfnXtEnxw77lIiNHjruf2BX33ZlADeUtFC7Y39OC7ly3CqoLkaX9/fEwUHvhcMbZ9/WykxBpw81N7UXGMoaxGPUNj6HKOoSQCpisCSk51fuO0hRq12F245vFdGHB58OTNK/D4jcthTTJO+zrWJCN+de0StPWP4Dt/qYc8eVFsFoIWyPvbBvCztxtwcXEablidO6trLcqIw/O3rUa+xYRb/7gXO471BmmUFC6B0FLzhpDJFmfFQ5K4Y0+NJsL4H/D4/HjuttU4b0HqrK63Mj8Jd2+cj9drO/FiVXuQRhmkOWTniBd3PbcfaXHRePDzZUE5FSLZbMCfvrIK1/9hN279YxV+f+NybJifEoTRhobfL8M2MIo+l/vkoqUXjpGJspl5qbG48Kw0mAxhqTIUQp3NCY0EFGfGKT2UoDEbdChMMaO+Y27NI3t9flQ02lHV2o/Y6CgkGqOQaNQjyaRHokkPa6IRep24Wxqaeodx7eO7MO6X8dxtq7AwPTg/k3eeV4RdLX34/usHUW5NxIL02Flfc9YJIcsyvvVKHboHx/Di7WsQbwzeAk6y2YDnbluN6/+wG7c9XYXHb1iGc2f5yRZs7nEfXtvficc+akJTr+sTX5ckQJaBmCgtNp6VhsvLMnHO/BShf4CDob7DiaJUc8R9CJVmxWNHox2yLKvuOKrp8Ptl7GsbwGs1ndhW34V+l+fUz/JkFrMBN6/Lw5dW5Qb19z8YGnuGcd3vd8Hnl/H8bauDEpoBWo2E/91cjkt/uQN3PbcPr921Dkb97H7eZ/3b8syu43j74Al859KFWJqTONvLfUKSSY/nTj4pf/Xpajx2wzKct1D5UB4c8+L53W14orIF3YNunJURhx9dsRhZCdEfe3ow6XWoPj6A12o6sK2+C2/UdiI+JgqbyjLw9fPnIVWFPYLPRJZl1Nkc2DBf+fcp2Eqy4/GX/R3oHnQjPT7y3rsRzzge/6gZL1XZ0OEYhUGnOfUgsWFBCnx+eWLDhMuL/hEPeofceL22E//zzhH85v1GXLsyB7eenY+MeOUPs23sGcK1v98NWZbx/FdXn6rqCqbU2Gg8vHkJbnhiNx54/SAe/ELZrK4nTWdCevny5XJVVdWp/364axCXP1KJtUXJeOKmFSHd1OEY8eD6P+zG0e4h/OLqcmwqywzZvT7N4JgXv/ugCc/84ziG3ONYV5SM2zcUYn2R5YxPTJ5xPyoae/FaTSfeqj8Bg06D+z+zENevzImoDTGdjlGs/el2/PDyYty4Jk/p4QRV9fEBfP63O/H4DctwUXG60sMJqvcbevDdVw+gwzGKc+an4IryTFxUnA7zFP7KOdQ5iMc+asLWui5IAC4vz8LdF85DduL0F82Coc7mwJef3AuNJOH521ZhXgjC+F/9zzsNePT9JvzymnJcXp71ia9LklQty/LyM11nVoH8tWeqsLulH3//5gYkmw1Tvs5MOUe9uO2PVdh7vB8PbCrGTWvzQn7PAL9fxsv7bHjw7Qb0uTy4tCQDt59TOOO2ki12F777aj0qG/tQbk3Aj68swVkRMt/69oETuP3Zavz1zrVYEoK/mpQ06vFh8QPv4M5zC3HPRQuUHk5QdA+O4QdvHMSb9SdQlGrGf1+xeEZVUgDQ3j+CLRUteGFvG2QZuOPcwk+t8Q2FHcd6cfsz1Ug06fH0LStRkGIO+T3HfX5c9dud6Hd58OF950E76QFrqoE844nM9v4RvHuoG9evyglLGAMTZXFP37oSGxel4fuvH8RD7x4JasnJ6dS0O3Dlb3fi/pfrkJtswht3rcej1y2dVY/ffIsJz966Cg9vLkd7/wg2PVKBH795GCMe9Z9KUWtzQKeRsCgjMj5g/lWMXot5qWbUtKt/Yc/nl/HHna244KEP8ffDPbj3ovl48+tnzziMgYmSsAc+V4zt95yLC89Kw8PvHcMFD32Itw90heV39fXaTtzy1F5Yk4x45Y61YQljANBpNbhjQyFsA6P426HuGV9nxoH8x52t0EoSblidN+Obz0R0lBa/vX4prl6ejV9vb8R3/lqPcZ8/JPfqGRzDfS/V4opHK9HpGMUvri7Dy7evCdruHEmScMWSLPz9ng24enk2Hv+oGZsf24VRjy8o11fKzqY+lFkTIrZh1Kr8JFS1DsA9rt73SZZl3PdSLb7/+kEsyUnAu3efg7vOnxe0xebMhBg8ct1SPH/bapgNOtz+7D7csGUPjpwYCsr1/52nKlvwny/sxxJrIv78tTVhP8PxwrPSkJUQgycrW2Z8jRn96w+7x/Hnve34TEmGIgsbOq0GP/t8Ke48txDP72nHnX/ahzFv8H45Gk4M4r6XarH+Z+/j1ZoOfO2cArx/77m4aml2SFbWE4x6/OSqUjx2wzIc6HTi/lfqwvI0EQrOES/qbQ6sK4rcBjzriiwY9fpUvY36sY+a8Zf9Hfj6BfPw9C0rkZtsCsl91hQmY9vX1+OBTWehzubAxQ9/hFue2oudJytVgkGWZfz8nSN44I1D2LgoDU/fulKR7fo6rQY3rc3F7pZ+HOycWa36jAL5lWobhtzjuGVd3oxuGgySJOH+Sxbie5edhXcPdeOChz7EK9U2+Pwze5NlWcaHR3txw5bduOThHXijrhObV1jxt7s34NuXLprSwsZsXVycjvsvXog3ajvxmw+aQn6/UPhHcx/8MrA+ggN5dWEyNBJQqdKt/dsbuvGztxvw2ZIM3L1xXsjL93RaDb68Lh8f3HcevrFxHmrbHbjuD7tx6a8q8Eq1DZ7xmf+FW9lox+WPVuKR9xuxebkVv71+qaJ/mW1enoOYKC2erGyd0fdPe1Fvz569uOAXHyI+Jgqv/se6Gd002Cob7fjJW4dxoGMQC9Jicf8lC3D+wtQz/qC5x33Yd9yBisZevHOwG409w0iNNeCmtXm4bmUOEhU45UKWZdz95xq8WtOpypX8//fqAbyyz4aa710U0bXWV/6mEgDw1zvF+B2YqsaeIVzx6E7kJhvx8u1rEaMPf3iNeX14raYDf9jRgmMnf+c+szgd6+elYHVB0pTaW9bbnHjwnQbsOGZHZnw07r5wPr6wLDR/wU7Xd1+tx4t7bdj57fNhObm+FrIqiwef3YZbnqo6bXmHUvx+GW8e6MJD7x5Fi92F5bmJuOPcQiQYo+CXJ74un3zdoa5BVDTasbu5H6NeH7QaCUtzEnDtyhxcVpqpeJCMeX3Y/Ng/0NgzjFfuXBu0nUXhcP5DHyA3yYgnb16p9FBC6qF3j+A3HzRh//cuRFwI++MGk2PEg8sfrYTLPY7X71qPzBC0j5wOWZbx0TE7nt7ZisomO8a8fmg1EpZYE7B+ngVLcxIRpdVAI000IJMAeH0ynt19HNvqupBojMJd58/D9atyhFqvaOwZxsZffIhvXjgfX79gHoAQBvKiOx7F0e4hVHzrfESFoYP+dHl9frxUZcPD7x1Fz9Dpu3IVpJhwdpFlWp/K4XTCOYbPPVIBQ5QGr/3HelWcSReoP/7uZxfhK2cXKD2ckNrV3IdrHt+F39+4HBeelab0cM5o3OfHTU/uwZ6Wfrzw1dVYlpuk9JA+xj3uQ/XxAVQcs6Oi0Y76Due/3RUIAEa9Fl85uwC3nZ0v3O9twE1P7MGhrkFUfut86HWaKQfytCZGx7x+7Dhmx30XLxAyjAEgSqvBdatycOWSLFQfH4BflqGRJEjSxDZmCRJyko0haS4dTOnx0XjshmXY/Pgu3Pmnajxz6yph/80DAnOqc+FEjSU5CYiJ0qKy0a6KQP6vbYdR2diHBz9fKlwYA4BBp8XaQgvWFlpwPyaO/zraPQSfLAMyJv7KlSf+yl2cGRe2UtuZumV9Pm56Yg+21XfiyiXZU/6+aQVyn8uNOJ1GFWekxei1qg+GJTmJ+MmVJbjnpVq8Um3DNYL/u1c22mEx67EgxLuiRGDQabEyPwkVKljYO9DhxFM7W/HltXm4eoVV6eFMSaJJP6t6aKWdM8+CwhQTnqxsxRXTmNqd1iPXwIgHVy7JUsWfz5HiqqVZmJ9mxgt7g9fiLxRkWUZFYx/WTWELeaRYX2RBY88wTjjHlB7Kp3phbxsMOg3u3jhf6aHMGZIk4eZ1+aizOVF9fGDK3zetQJZl4MsKlrrNRZIkYfOKHNS0O9BwYlDp4ZzW0e5h2IfdEV1/PFng/6vI5W+jnoluhJeWZAjXiS3SXbU0C3HRummVwE0rkM0GnapW/CPFlUuyoNdq8MIecZ+SA3+6z6VAXpgei2STXuhA3lbfhSH3ODarZKoikhj1Oly7KgdvHzwx5e+ZViBbzJyqUEKSSY+LitPw1/0dQd2RGEyVjXYUWEzCL5YGk0YjYW2RBRVB3HUWbH/e24Z8iwmr8sVbyJsLblyTh0UZU19TmVYgi1piMhdcuzIHzlEv3pnGp224eH1+7Gruw9oi9S7CzNT6omT0DLnR2DOs9FA+obFnGHtbB7B5hXXOzOuLJishBlv/z9lTfr3YdVR0ypqCZFiTYoSctqhpd2DE44vo7dKnE5iiEbHa4sWqdug0Eq5aKs4GLvp0DGSV0GgkbF5uxT+a+9Bq/+RRUUqqOGaHJAFrCuZeIGcnGpGXbBRuHtkz7scr1TZcsCgVqbGRd7JJpGIgq8gXllmhkRDUU26DobLRjtKs+Dm7ir+uyIJdzf3whqgN7Ey8d7gbfS6P8LXr9HEMZBVJj4/G+QtT8VK1TZhf/qExL/a3R3a7zTNZX2TBsHscdTZx2nG+sLcdGfHROGeeuCe10ycxkFVm84oc9A658X5Dj9JDAQDsaemHzy/PyfnjgDWFyZAkoOJYn9JDAQDYBkaw41gvvrjc+omjhEhsDGSVOW9BClJjDfizIDv3KhrtMOg0WJobWWfnTUeCUY+SrHhh5pFfrLIBAK5ePvUeCiQGBrLK6LQafGFZNt4/0oMu56jSw8HOxj6szE8Sqv2hEtYVWbC/fQAut7JnIvr8Ml6qasfZ81IUO/GZZo6BrEKbV1jhl4GXTz4JKaV7cAxHuoewtnDuTlcErC+ywOuTsatZ2WmLj471oss5hmu4M0+VGMgqlJtswtrCZPxlf4ei43izvgsAVNF+MtRW5CUhLlqHN+uV3bjzl30dSDbpsXER3xM1YiCr1Ib5KWixu2AfPn0T/lDbWteFhemxKEoNz1HrItPrNLi4OB3vHjqh6GnUVa39WFtkUfzUG5oZvmsqVW5NAADUKHTycYdjFNXHB7CpLFOR+4vosrJMDI2N46OjyizudQ+Oocs5dupng9SHgaxSJdnx0Gok1CpU+7qtrhMAcFlphiL3F9HawmQkGqPwRm2nIvfff/LDmYGsXgxklTLqdZifFouadmUCeWtdF0qy4pGbbFLk/iKK0mpwyeIMvHe4G6Oe8E9b1LQ7EKWVUJzJFrlqxUBWsXJrAmraHfD7w9v68XifC3U2J5+O/41NpRkY8fjw/pHwb9ypbXdgUUbcnC9BVDMGsootsSZgaGwczWFuNrS1bqK64rMM5E9YVZAMi9mArXXhnbbw+WXU2RycrlA5BrKKleecXNgL87TF1rouLMlJ4MaDf0OrkXBpSTq2N/SEdZNIY88wXB4fA1nlGMgqVphihtmgQ0371A9RnK2m3mEc7hrEZaWsrjidy0ozMeb1473D3WG7Z+BngIGsbgxkFdNqJJRmx4f1CXlrbRckCfhsCacrTmd5biLS46JPTe2EQ027A3HROuRxkVXVGMgqV25NQEPXUNjO2tta14kVeUlIj2fT89PRaCRcWpKBD4+OysCXAAAGFUlEQVT0YnDMG5Z77m9zoMyaAA27u6kaA1nlyq0JGPfLONjpDPm9jpwYwrGeYWziYt4ZXVaWAY/Pj78dDP20hcs9jqPdQ1jC6QrVYyCrXGDOcH8YduxtreuERgIuWcxAPpMl1oSJAy7DUG1R3+GEX/7nIi+pFwNZ5VLjopEZHx3yeWRZlrG1rgtrCpOREmsI6b0igSRJuKw0AzuO2eEY8YT0XoH3viybgax2DOQIUJ6TEPJAPtg5iBa7i9UV03BZaSbG/TLeORjaDnA1bQ7kJBmRbOYHpdoxkCNAuTUBtoHRkHZ+21rXBZ1GwiXF6SG7R6RZnBWH3GQj3qgNbbVFLTeERAwGcgQot04cnxSqzm/ucR9errZhw/wUJJr0IblHJJIkCVeUZ6GyyY7WEO2mZIe3yMJAjgAlWROd30I1bfFGbRfsw27cvC4/JNePZNevyoFOI+Gpna0huf6pDm9c0IsIDOQIEKPXYkFabEhaccqyjC0VLViQFot1RclBv36kS42LxqbSTLxY1Q7naPBrkgMd3s7KYIe3SMBAjhCBhb1gd37b1dyPw12DuGV9HiSJmw5m4pb1+Rjx+PBiCE4Kr2kfYIe3CMJAjhDlIer8tqWiBUkmPS4vzwrqdeeSxVnxWJWfhKd2tmLc5w/adX1+GfU2J+ePIwgDOUIEdmkFcx651e7C3xu68aVVOXwCm6Vb1uejwzGKdw8Fb+fesZ4hdniLMAzkCFGYYkZskDu/PbWzFTqNhC+tzg3aNeeqjYvSkJNkxJaKlqBds4ZHNkUcBnKE0GgklFqD1/nNOerFi1Xt2FSWidQ4NhKaLa1GwpfX5qH6+EDQ3qOadgfiY6KQb2GHt0jBQI4gwez89uLedox4fLiFpW5Bc/UKK2INOjwRpKfkmvaJDm9cbI0cDOQIUm5NxLhfxoGO2XV+G/f58dTOVqzKT8LirPggjY7MBh02r7DizfoudDlHZ3WtQIc3TldEFgZyBAn8cs62Mfq7h7rR4RjFrev5dBxsN63Ng1+W8fQ/js/qOm8dOAG/DLbcjDAM5AiSEmvA9aty8NTOVrxW0zHj62ypaEFOkhEXLEoL4ugIAKxJRlxcnI7ndrdhxDOzM/cOdDjx3VfrsSw3EeuKLEEeISmJgRxhvr+pGCvzknD/y3Wot01/6uJvh7pRfXwAN6/Lg5anT4TErevz4Rz14ncfNE37e+3Dbnz16SokGvX43ZeWQa/jr3Ak4bsZYfQ6DX7zpaWwmA346jNV6Bkam/L3vt/Qg//40z6UZMVj8wprCEc5ty3LTcQV5Zn41fZG/O7DqYeyZ9yPO56tRp/Lg8dvWM6+1BGIgRyBLGYDHr9xGRwjXtzx7D64x89cdfF+Qw++9kw1FqTH4tlbV8Go14VhpHOTJEn4+RfLsKksEz99q2FKoSzLMr7/+kHsbR3A/3yxDCXZXGyNRAzkCFWcGY+ff7EM1ccH8L1XD0KWT9/jYntD98fCON4YFcaRzk06rQb/e/XUQ/nZXcfx/J423HluIT5XxkMCIhUfgyLYZ0sz0HCiCL/e3oj8FBNuWJ0Lk+Hjb/n2hm7c/sw+LMyIxTO3MIzDKRDKEoCfvtUAALh9Q+HHXuMe9+Gjo3b84I1DuGBhKu69aIECI6VwYSBHuLs3zkfDiSH89K0G/PStBmTGR6Mw1YzCFDOSTHo8sr2RYawgnVaDX1xdBmAilE84x2CI0qCpx4Wm3mG09Y/A55dRlGrGw9eUQ8OF1ogmfdqfspMtX75crqqqCuFwKBQ84358cKQHx3qG0XjyP029wxjx+FCaHc8wFsC4z497XqrFazWd0Gs1yLMYUZRqRlGKGYWpZpw7P5XvkYpJklQty/LyM76OgTw3ybKMniE3LGYDy9sEEXhPkk166LRc3okkUw1kTlnMUZIkIY1Ng4TC94T4MUxEJAgGMhGRIBjIRESCYCATEQmCgUxEJAgGMhGRIBjIRESCmNbGEEmSegHM7qgDIqK5J1eW5ZQzvWhagUxERKHDKQsiIkEwkImIBMFAJiISBAOZiEgQDGQiIkEwkImIBMFAJiISBAOZiEgQDGQiIkH8f10+bBFhJniOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc20c5d6190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc20c55df90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc20c4cde50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    input_ = i # 0-3\n",
    "    weights_ = np.hstack(weights[input_])\n",
    "    weights_ = np.reshape(weights_,[31,1])\n",
    "    weights_ = np.concatenate([np.hstack(np.flip(weights_[1:,:],0)),np.hstack(weights_)])\n",
    "    plt.plot(weights_)\n",
    "    plt.xlim(0,60)\n",
    "    plt.tick_params(\n",
    "        axis='x',          # changes apply to the x-axis\n",
    "        which='both',      # both major and minor ticks are affected\n",
    "        labelsize=18.,\n",
    "        bottom='off',      # ticks along the bottom edge are off\n",
    "        top='off',         # ticks along the top edge are off\n",
    "        labelbottom='off') # labels along the bottom edge are off\n",
    "    plt.tick_params(\n",
    "        axis='y',          # changes apply to the x-axis\n",
    "        which='both',      # both major and minor ticks are affected\n",
    "        left='off',      # ticks along the bottom edge are off\n",
    "        right='off',         # ticks along the top edge are off\n",
    "        labelleft='off') # labels along the bottom edge are off\n",
    "#     plt.title('Filter of input branch ' + np.str(input_))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For linear phase filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc20c2fb510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2042aaf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc20c427050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SG3l2CQPXDu+CEPDfr04GHPP657nodYJbL266q0qhONe0S1EAiI+MZ91t6xjffTxz3p7D0m+Weo+VWrT9G4mRiRh0BpZdv4y5F8zljzl/5A+b/uANIvsihOBPE//Enyf/mWXfLvM7xsOcYXNYfuNydpzcwbSl07wi5MFjKYzvPp4N8zZQUlvChEUTOFx6uJk/hfMHKSWnKizU1isxEA4Op4tvTpQxskdDsQ+X9Pgoxmcks/qrk37Tmi12Jyt3Hmfa4LSzTkNVKM4l7VYUAGIjYln7o7VM7DWR+avne7ONPBfpuIg4APQ6Pa9e+yp3XHQHj338GPuL9/u1AgD+97L/5anLnwK0GEYgbhx8I2/e9Ca78ncxdclU774GqGuJXNz1YjbO20iVrYoJr07gQPGBs3/j5xl5ZbXc9Px2xjy+keGPfMSLW5smrnsLKqmxORnR8+xFAeD6i7qRW1LDl8dKGxx7/9t8ymrsQQPRCkVbo12LAkC0KZr3bn2PaRnTuOPdO3h+5/OUWcqIi4irswtZr9Oz8OqF3DvqXoCAogDwy/G/ZOddO7lzxJ2NnvvagdeyevZqvj/9PZMWT6KwWtvQVD9IPSJ9BJvnb8bmtDFh0QR+KPzhbN7yeUW11cHcl3ewr6CSX18xkAn9O/HnD35g0SdHwp7rq1zt4j2iGSwFgCuGdibKqGflzrr7hqSULP3sGH06RTMu4+z7LygU55J2LwoAUcYoVs9ezcz+M7n3/XtZtWeV34u+Tuj411X/4tVrX+XukXc3OufILiMxG4PXqLmq31WsuXUN+4v3M3Gxlm1Ubi1vcP4L0i4gZ0EOAJmLMvnu1Hehv8HzmGc37OdQYTUvzB3JvRMzeOG2kUwZmMrja/eSWxw4A8wfXx4rpVNsBN0Sm6cuUEyEgRtGdmX1V3kUlFu8z398oIivj5dx+/heHbbnsKLj0iFEASDSEMlbN7/F9QOvJ68yL2A8QAjBguEL6J3Yu9nOfXnG5Xww5wOOlh3l0lcvxSVdfs8/uNNgtizYgklvYtLiSXyV/1WzraEjUlJtY+lnx7hhRDfGu1NIdTrB47OGoReCZzbsD2u+7/MquLBbfLNeqO+ZkIFTSl78WHNpSSn52/r9dE2I4ubR3ZvtPArFuaLDiAJotZJW3LiCu0fczYx+M87puSf1nsS629Z5XUiBRKl/cn+2LNhCtCmayUsm88XJL87lMtsVr+84hsXu4p7Muk1w0uIimX1xd977No/TlZYAr66Lxe7kcGEVg9PjmnWN3ZPMXH9RV5ZsP8qXx0p5Yethvjlexv1T+jXohaxQtAc6lCgAGPVGXrj6Bf485c/n/NyX9riU9XPXMyx1WKMVWDOSMti6YCuJkYnNWuivI+F0SZZsP0Zm/05+m8po/Xwlyz9vUAfML/sKKnFJrXZ+c/O7qwbROT6SG/7zKU+s3csVQzpz0yi1L0XRPulwotDajOk2hm/v/ZaL0i9qdFzPhJ5svX0radFpTFs6ja3Htgadu9xSft64nL48VsrpSmvAi2ufTjFc1i+F5Z/nBqx068uefG0/yeD0hs1pzpbEaBP//eklPDR9AH++fij/mHORiiUo2i1KFFqRbnHd2LJgC93ju3PFsiuCFvr72/a/MXLhSF7e9fI5WmHrsX5PASa9jsz+nQKOuXZ4V/LKLXx7ojzofHvyKoiJMDRbkLk+KTER3DepLz8a01PtXla0a9S3t5XxFPrLSMpg5hszWXdwXcCxp6pOIZHcueZO/v3Fv8/hKjWOlh3lnjX3cLr6dLPMZ3PauHnVzWw4vKHO81JKPtpzivF9kxvdeTx1UCp6nWDd7oKg5/ohv4JB6bHogrRNVCjOd5QotAFCLfRXaimlT2Ifrhlwjd9Cf2dDta2a53c+32ihv/WH1rNw18JmK/R3tOwoq/asYubrM/ngwAfe5w8VVnOsuIbLg/Q7SDCbGNM7KagouFySH/Irmj3IrFB0RJQotBFSzClsmreJC9MuZNbKWWwtbBhjKLOUkRqdyqqbVnHDoBt4YN0DPPnJk81y/vWH13Pv+/cy4/UZVNn89/n27BQ/WnaUzEWZnKgIXiW0MUprtc1kUcYorlt+HduKtgGw86i2O3xcn+Abv6YNTuNQYXWjexZyS2qotjlbJMisUHQ0lCi0IRKjElk/dz2ju4wme0+230J/iZGJmPQmv4X+glFhrQjafjTnaE6jhf4MOgPr564Pq9BfmaWMkxUNC8d5ROa1Wa8xIn0Ef9rzJ1btXsXOY6UkRZsCVh/15dJ+2v6FTw4VBRzzgzvIPEhZCgpFUJQotDE8hf6Gxg/1W+jPs//BX6G/xrJwrA4r3Z7uxuy3ZmN32hsc91ygX7z6xUYL/SVEJnBJj0vCKvR3/4f3M+hfg/gk9xO/5+yd0JuP5n7EoNhBzH5rNh8cWsWIHokhZfBkdIohNTaCTw8VBxyzJ78CvU74TW1VKBR1UaLQBomNiOWJYU/4LfTnuymufqG/32z4TUBhKK4tptJWycrdK7n5zZuxOeu2kiytLUUg+PFFP2bVTavYlb+LKUum1Cn059uNLpxCf3mVeVTaKpm+bDo5R3POnNNdzTYhMoG4iDievOBJxnW7lB8sj2OP2BTSZyWE4JK+KWw/VBTwve/JqyCjUzSRRrWZTKEIhhKFNkqUPspvob/6NZV8C/09+emTPLDuAb8XR89d+bSMaazeu5pZK2ZhcVjqHPd0o7tu4HX895b/svv07jqF/uqXEw+10F+ZpYzRXUbTM6EnV712lTfbyLMmz5xR+ih+PWoRka7hLNn3axZ+uTCkz2pcRjJFVTb2nar0e1wFmRWK0FGi0IapX+jP4XL4LZ/hKfR3/5j7+fuOv3PfB/d524p68AR1Hxz7IM/PeJ73D7zPtcuvpdZeC5xpUephRv8ZvHvru95CfwVVBXU61nkIpdBfaW0p/ZL7sXn+Zvom9WXm6zNZe2AtZZYyTHpTnX7XRwsdpNr+wPSMK7nnvXv41+f/Cvo5eQLSnx8paXCstNpGXrlFxRMUihBRotDG8RT687QcTYry30ZSCMEz05/h4fEP85+d/+GeNffUEQbPXXliVCL3jLqHl695mfWH1jPj9RlU26obdKMDzarwFPrLXJRJbnlukwr9eSyc1OhUNs/fzOBOg7luxXV8cOADEiIT6sQO9p2qpHtiPO/M/i/XDriWn639GU9vf7rRz6hbYhRd4iPZ4UcUPEFmlXmkUISGEoV2gElvYvkNy3np6pca9KP2RQjBE1Of4A8T/sBLX73E7e/cjtOldSrz9d8D/PiiH7Pk+iVsObaFK1+7khMVJxpYAXCm0F9eZZ5WfTYivEJ/Uso6sZBkczIb521keOfhfHf6uwbusAOnqhiQFkuEIYJVN63ixsE38suPfskT255o9H1f3DuJz4+UNHCd7VGZRwpFWChRaCcY9UbuGHGH3wu3L0IIHpn0CI9MfIQl3yxh7n/n4nA5GvjvAW674DZen/U6nx7/lG9OfROwsqun0F9CZAIZSRkBz+2v0F+VrQqndNaZ25N6m9kzk2Fpw7zPO1ySw0VV9HNnCRn1Rt644Q3mDJvDbzf+lke2PBIwmHxx72QKK60crbdfYU9+BWlxEaTERDT6uSkUCg1Day9A0TL8IfMPRBgi+PWGX2Nz2hjcaTDQsKT3LUNvwag3MvvN2aRFB95BPLbbWPIezKvj//eHp9Df5MWTmbZ0Gi/MfAFo2OkuLiKOzfM313nuVI3E7pQM6Bzjfc6gM7DkuiUYdUaycrKwOqw8NvmxBumqY/pobrUdh4vr7G/47kQ5Q7o0fxE8haKjokShA/PwJQ9j0pt4YN0DvH/gfcxGMya9qcG4WYNm8fVPvm5UFEALfIeCp9Df5CWTmfvfuYD//hL1L+wnq7QYSL/UuvsJ9Do9r1z7Cia9ice3PY7VaeWvl/+1zuv7pESTEmPi8yMlzL64BwCVFjsHC6u4+sIuIa1boVAoUejw/GLsLzDpTdz3wX10iQ18cfRYEs2Fp9Df1KVT+f7090HdXgAnK10IAX1TYxoc0wkdz898HpPexN+2/w2b08bfr/i7Vxg8cQXfYPN3J8uREi7s7t8tplB0FES2yACuAAYCnh9bKbAX+FBmyUOhzqVE4Tzgp6N/SnJUcrNVNw0VT6G/l3a9xLhu44KOL6yVpMdFBtxkphM6/nHlPzDpTTzz2TPYnDb+PePf6IQWGru4VxIffFfAidIauiWa+ea4VlL7wm7KfaTomIhsEQW8AMwB7MAhNDEAGA7cAfxdZIvXgHtklgzaqlCJwnnCLUNvaZXzpphT+M2lvwlpbHGti65B+h0IIfjbtL8RoY/giU+ewOa08eLVL6LX6bm4t7Zf4YujJW5RKKNnspkEc0OXmULRbjBjENlip88zC2WW9OzsfBKYBtwKrJZZsk4NG5EtjMC1wD/cY38e7HRKFBRthqJayaCeweMWQggen/I4Jr2JR7Y+gs1pY9F1ixjQOZa4SAOfHizm2gu78mVuaUiVVhWKNk0NDpklRwU4Ohv4ucySq/wddIvEm25xeA4lCor2gsPpotQqg1oKHoQQZE/KxqQ38fvNv8fusrPs+mVMGpjK+h9OcXNuKYWVViYNDNy5TaHoAERwxl3UGGXusUFRoqBoE5yqtOKS0DXBHNbrfjfhd0QYInho/UPYnXZ+Muw53vk6j5ue345RL5g0ILWFVqxQtAk2Ao+JbLFHZslcfwNEtugOPAps8He8PkoUFG2Ck6VaDaZQLQVffjX+V5j0Ju7/8H5sThtj+jzEjsNV/CQzQ8UTFB2d+4CPgEMiW3yBlm3kqXkfj5aNNNr9/M9CmTBkURAiSMqTDD3lSaGoz8kybSdy14TwRQHg52N+jklv4t737+XyPjbW/M8yhnZRriNFx0ZmyTyRLYYDN6Fdn4fQ8Pr8D+BNmSUdocwZVBSECDHlSbhTnmTwlCeFoj5eS6GJogDwk1E/waQ3cee7d3K/azZrbl1DtCl49zaFoj3jvti/4X6cNaKxbl0AQoh/oKnQ/wCrpayX8iTqpDytklL+3OfY3cDdAAaDYeT69eubtMiqqipiYhpuaOrInG/v+dXvrXx5ys4/p5z9e15/aj1P7H2CofFD+cvQv2A2hBenOJecb39nUO85XCZNmlQjpTx3dzdSykYfQCFwawjjbgUKAx03m82yqWzevLnJr22vnG/vee7LO+TEP3/QbPMt/2651Gfr5biXxsmy2rJmm7e5Od/+zlKq9xwuQLUMcv0N9uBPTOBPbAplbChVUps95UmhqE9hpZX4iOA9mUPllqG3sPKmlezM28nUpVPrtBVVKM5DOgGZoQwMJdCspTwJsUfKAClPIryUJ4WiPkVVVgbFN58ogFbo7+1b3uaGlTcwZckU1s9dT4o5pVnPoVC0JiJbzAtx6OhQ5wxFFM6kPInmSXlSKHxxuSQl1TbiOzV/hvTM/jN5d/a7XLfiOiYtnsSGuRtIi2m8GqxC0Y5YBEgglDuqxgPIboK6j6SUeWhZRvOAA2gpTzPdj6HAQfexEe6xCkVYlNbYcLokcabmtRQ8TO87nfdufY9DJYeYuHgieZXqa6roMBQALwGxQR6hWhSh7VOQsnlTnhQKX4qqbADENWNMoT5T+kzhw9s+ZMbrM8hclMmmeZvoHt+9xc6nUJwjtgMjZZasbmyQyBa1oU6o2nEqmoXTlRa27C/E6nCG/dqiKitAi1kKHib0nMC629Zxuvo0mYsyOVp2tEXPp1CcA1YAh0MYtwd4JJQJm00UhBAThBCbmms+Rfthb0EFlz+9lfmvfM6N/9ketjAUV7sthRYWBYDx3cezYe4GSi2lZC7K5FCJ2oivaL/ILLlSZsmbQxj3g8yS2aHM2ZyWQsgpT4qOg5SS/337O4x6wUPTB/DdyXJe3nYkrDnKa7X9kNHnqEzR6K6j2TRvE9W2aiYsmsC+on3n5sQKRTsglDIXzZ7ypOg47DxWyq7cMh67bii3je3JZ4eLWbr9GPdMyECvC+3Ov7xGsxSijS1vKXi4KP0iNs/fzJQlU7QYw/xNzd6SVKFoLUS2GADskVnSfxvDRggl0LyIZk55UnQc3tx5gpgIA7NGdAXg5lHd+Z83vuLr46Wqb1g+AAAgAElEQVSM7JkU0hzltXaijHqMIYpIczEsbRg5C3KYsmQKExdNZMO8DVyQdsE5XYNC0YI06QcVivuo2VOeFB0Dl0uyce8pJg1MxWzS7i8m9OuEXifYvLcw5HnKa+3ERxlbapmNMrjTYLYs2IJJb2LS4knsyt/VKutQKFqAJt2khyIKWsqTlNWNPYCQU54UHYPdeRUUVdmYMvBMI5t4s5FhXeP5/EjoZSXKalpPFAD6J/dn6+1biTXFMnnxZHac2NFqa1EoWptQRKHZU54UHYPPj2oX/rH1+iAP757AdyfLcThdIc1TXmsn3tx6ogDQJ7EPWxZsIdmczOVLL+eT3E9adT0KRWsRyo7mlVKGkPIk5Q9ShpbypOgY7DxaQvekKDrHR9Z5/sLu8dTanRwsrAppntZ0H/nSM6EnWxZsIT02nenLppNzNKe1l6RQnHPU5jVFk/nmeBkXdU9s8Pzg9HgA9hVUhjRPea2dhDYgCgDd4rqRMz+Hngk9ueq1q9hwWNV4VJxfNFkUhBADhBDhb19VdAjKamzklVsY3CWuwbGeyWaEgMOFje6891JlcRAT2XbahafHprN5/mb6JvVl5uszWXtgbWsvSaE4Z5ytpXBucwgVbYY9+RUADE5vKAqRRj1dE6I4XBRcFKSUVNscxES0HVEASI1OZfP8zQzuNJjrVlzHu/vebe0lKRTh0mIpqY2h9iWcp+x3u4YGdo71e7xPpxgO+4kpnK6wsPNoiadbH1aHC5eEKFPYe2xanGRzMhvnbWR45+HcsPIG3trzVmsvSaEIlXzgrqa8UMUUFE3iSFE1sREGOsX6b7bXIymKk2V1s5SllNy5ZCc3Pr+dZTu0fk01Ns0DGW1qW5aCh8SoRD667SMu7noxt7x5C298pwoFK9o+MktWyCz5clNeq0RB0SQOF1XTu1M0Qvi3UNPjoyirsVNrOxN2+vJYKd+eKAdgxReaKFRbHQCY26Cl4CE+Mp4Pf/Qhl/S4hNv+exuLv17c2ktSKFoMJQqKJnGkqJreKdEBj6e701QLKize53a4N7TdM6EP35+soLzWfsZSaGMxhfrERsTywZwPmNRrEre/czsv7XqptZekULQIbfuXqGiT2J0u8spqmTWiW8Axnr0L+eW1XvH4KreMPinRjO6VxAtbD3PIJ+bQFmMK9Yk2RbPm1jXMWjmLu9bchc1p46ejf9ray1IoENliMnAlWntkT554KVqb5A9kltwc6lxKFBRhU1BuwSWhW0JUwDHp8VHesR5255UzpncSGakxABw6XeUdF20yUNOCa24uooxRrL5lNTetuon7PrgPm9PGL8b+orWXpejImDGIbLHT55mFMksuBBDZIgn4L3AZcAT4wf1f0MTheuCXIltsBWbJLBm0/szZioJKST0PyXMHkNMTIgOOSYvTAtCnKrSualaHk4IKC71SoumeGIVJr+NgYZV3J7PZpG8XogAQYYjgzZvfZM5bc3hg3QPYnDYevuTh1l6WoqNSg0NmyVEBjj4HdAbGyCz5hb8BIluMApYBfwfmBjvd2cQUmpzypGjf5JVrotClEUvBbDIQYdBR5u6VcLK0Fimhe6IZg15H5/hICsot3phCWw40+8OkN7H8xuXMHjqbX2/4NY9uebS1l6Q4P5kJPBxIEABkltwJ/Aa4OpQJm2wpSCkrgCalPCnaN3llmkuoS3xgUQBINJsocbfaPF6qCUn3JLN2LFo7Vm3Tso/aeqDZHwadgWXXL8OoM/LHnD9ic9p4ZNIjATOyFIoWwEVoHhvhHhuU9vdLVATkdKWF97/NZ1B6XIPKpc1JfnktCWZj0OBwgtlIaY3WavN4ieYc6p6kCUlytIlTFRZqrO3TUvCg1+l59dpXMelNPPbxY9icNp6Y+oQSBsW54h3gbyJbFMos6be0r8gW44G/osUegqJEoYNwqsLCtf/8xJsC+tRNF3LjyMDZQWdDUaWNTjH+N635kmg2ed1HpyssCAGpsZHeY3vzK3zcR+33q6jX6Vl49UJMehNPfvokVqeVZ6Y/o4RBcS64H1gFfCyyRQFatlGZ+1g8WjZSZ2A98EAoE7bfX6KiDo++t4eyWhsr7h7L0+v3k71mN1MHpZJgNjX7uYqqrKSEIgrRRm+l1JIaG4lmk7dvc3KMiZIaGxaHE6NehNzPua2iEzr+ddW/MOlN/H3H37E5bfzzqn+iE2orkKLlkFmyApgussU44ArqpqQWoXXNXCuz5GehzqlEoQNwpKia977N52eT+jKmTzJ/vHowM57bxptfnuDOy/o0+/mKqqwM7RofdFyC2USZ231UWm0n0aeRTqLZhMXuoqLWjlHfMS6cQgiemf4MEfoInvz0SWxOGwuvXqiEQdHiyCy5Ha1L5lmjRKEDsPyLXAw6wbxxPQEY0iWei3ok8N+vTraIKBRX2UKzFMxGymrtSCkpqbaRFH3Gakl2//t0pRVDO7cSfBFC8MTUJ4gwRPDo1kexu+y8cs0r6HXtM2aiOP9QotDOkVLywXf5XNovhdS4M/sGpg3uzP99uJf88lrvBrHmwGJ3Uml1BCyE50tClAmnS1JldVBaY6OHO/MItCA0aKJgMnSsO2khBI9MeqROVtKS65Zg1LeNRkKKjoXIFl2BHwNdgH3AYpklS+uNGQT8S2bJycHm61i/xvOQ/aeqOF5Sy/Qhnes8P3lgKgDbDhQ16/mKqrTNaCkxwWMV5gjt7rjW5mxgKXgyl6osdgy6jvk1/EPmH/i/qf/H8u+XM/ut2dicttZekqKDIbJFP+A74GG0Xc1PAPtFtrim3tA4IDOUOTvmr/E84vOj2q71SzJS6jzfLzWGuEgDu3LL/L0sKFJK3vg8lxe2HPL2PgAtNgBaTCAYnjTTapuT0hobiT6iEGFwi4LVgdHQcdxH9Xn4kod5ZvozvP3D29y48kasDmtrL0nRsfg/NOugh8ySQ4HuwFrgbZEtHmzKhMp91M758mgJqbER3vx/DzqdYETPRHYdKw3wysZ579t8fvv2dwAkx0R401srLJooxIfQUznKqH29Ciut2J2yTqA5wu0yqrI4SIsLXC6jI/CLsb/ApDdx3wf3cd2K63j75reJMjafS09xXjMOuNvjLpJZshCYJ7LFduA5kS16yix5fzgTKkuhnfPF0VJG90rymxM/rGs8BwursDrCb6X90rYj9E2NIaNTNKt2Hvc+X1GriUJcCKLgsRQKK7W745iIM6+JNJ6xIgz6jmspePjp6J/y4tUvsu7gOq5+42qqbaH1r1YoghAFDcuGySz5H+AG4E6RLVYBId95KVFoIi5X63cizSur5WRZLaN6Jfo93j8tFqdLcrgwvAtQYaWVb46Xce2FXZg2pDM7j5V6LQTPf8MRhZJqTRSiTGe+bhE+weWOkpIajDtH3Mmi6xax+ehmrnr9Kiqtla29JEX7Zx9aLKEBMku+C0wDJgMhd4Y6P36NzYiUkqfW7aP/79dyzT+3eSuGtga7cjXX0KieSX6PD3D3T/ZsIAuV7YeLAcgc0ImxfZJxuiR78ioAqKjVahXFRQb3PHqCycXu+kdRxjNpmZE+/zacJ6IAMO/CeSy7fhmf5H7CFa9dQZWjYR9rhSIMPkSzBvymA7pLX0wAQs6JPn9+jc3Eu9/k8c/NBxnfN4VDp6t4cOXXdQKx55L9BZXoBPTvHOP3eO+UaPQ6wcHT4V14dp8sx6TXMSg9jn7u3geeOSosdnQitJ7KntIVxVVuUfB5ja+lYDoP3Ee+3DrsVlbcuILPT37OQ98+RGlt0+I+CgXwFDCdRq7lMkvuBkagWQxBUaIQBk6X5Jn1+xnSJY5XF4zm4SsG8tnhEm+byXPNwcIqeiSZvZk89THqdaTHR3K8NLxOBbvzKujfOcb7+miT/owo1NqJizKiC2HDmdlrKbjdRz7WQYTxzFevo6akNsYNg2/grZvf4lDVIaYsmUJxTXFrL0nRDpFZslJmyd0ySzbqspBZslBmyS2hzHn+/RrPgs8OF3O0uIafZGag1wluGd2dBLORNz7PbZX1HDhVRd/U2EbH9Egyk1sSnijsLahgcHocoG3E6psa42MpOIiLDG0Tlsd9VOS2FHwrofoK2fkQaPbHNQOu4dEhj7KncA+TFk/idPXp1l6SooMgssUAkS3CzzBBiUJYrPkmj5gIA5cPTgM0v/j0wZ3Z9MPpJmX4nA12p4ujxdX0TfXvOvLQI8nsLVsdClVWB0VVNnqnnJk3PT6KU+7qq5qlEFoms9noCTRrouAbR9DrBEa3GJjOo5hCfcYkj+G9Oe9xsOQgExdNJL8yv7WXpOg4NOlu6/z9NYaJlJKcfYVM6J9S5+J2+eA0Kq0OvmzifoCmcqy4BrtTen3+geieZKaoyka11RHSvB4B8S1J0Sk2gkL3TuYKi53YiNAsBYNeh8mg84pC/f4LHmvhfLUUPEztM5W1P1pLbnkumYsyOVFxorWXpOgYNCnYqUQhRA4VVlNQYeHSvp3qPD+6dxJCwBdHzq0oeNw5wSyF9HgtPdlzpx+M3ACiUFZjx+pwUmNzhtUQx2zSe0XBbKz7ukh3XOF8SUltjMxemay7bR0FVQVkLsrkWNmx1l6S4jxF/RpDZKe7nMTYPnXTP+OjjAzsHMcXR89tsPlQoSYKGUFEwdPU5nRlaOUVAlkKoGURWexOIsMRBR8hCGQpKFHQuKTHJayfu57immImLJrA4dLDrb0kxXlIyL9GIcRkIcRfhRBrhBDb3I817ucmteQi2wLfnCgjLtJA75ToBscu7BbP7rzyc5qamltcQ0pMBDFBeht7LuiF9URBSklpdcMCbXllFswmPfE+JSk8XdYKK61Y7C4iA2Q7+cNTAVWIummocCYDqSOVzj5bxnQbw6b5m6iyVTHh1QkcKD7Q2ktSnGcEjRgKIZLQenteBhwBfnD/F7QOP9cDvxRCbAVmSSlLfF57N3A3gMFgICcnJ+wFPv2lhVNVDgzbPkAnBClRgmEpei7rZsB4Di8mn/xQS49owZYtDbO6DFV2SmvsrF63mcTI5rnrraqqavTz2n3UQqxOBv1Mq2yaUH361W5iS/d7n1+6x0rOcQd3DItgfJczX4M9hy3EGlx15s0t14Lom7bvpKLGSmlhATk5obnLLBYtU86ko8FnZ3cfKzytzRfsPXdEAr3nJwc/ya++/RVjF47l6Quepmd0z3O/uBZC/Z3bNqGkkTyH1uNzjJTyC38DhBCjgGXA34G5nuellAuBhQDR0dFy4sSJYS/wk+o9fH0gl+TkTjhckoOnK1myp4adpZEsun10nR4CLYXD6SJ//YdcOaIXEycOanA8+mgJy37YTnyvIUwcmNYs58zJyaGxz+vxr7bQr1s0EyeOanQeKSUPbvmQuLRu3rWfLKtl44ebANiQb+C3t2Z6aye9sP8zekS4mDhxvHeOPsU1PLJ9M90zBuL8/nv69OzOxImDQ3ofsbu2UFBdRUykqcH7Sd79Cccry+jZrSsTJw4N+p47IoHe80QmMm7MOCYvnsxDex5i47yNDEsbdu4X2AKov3PbJpTb2pnAw4EEAUBKuRP4DXB1cy3Mw+9mDOa+4ZE8P3ckL80fxeZfTeTFeaM4WlzNHYt3npNU0KPuTJ/+AfYEeDKADp0+d0XO8sstdEkIXmlTCKFlD1WccR9tO1AIwILxvThcWM0Bnx3PhX76L0e7+yJUWx1Y7M4GsYHG8PRe9vcaFWhunMGdBrNlwRaMeiOTFk/iq/yvWntJivZFi6WkukKcXLjHtihCCC4fnMYztwznu5PlvPTxkeAvOksOntZqB/VP8y8KCWYTCWYjR4vPjShUWR1UWhx0jg/NSkqKNlFScyZ+sP1QMamxEdw2VnNJfHui3HussNLaoKtatDtuUVZjxyXr7jcIhs5tgdSPJ8CZec6n2kfhMiBlAFsXbCXaFM3kJZP54mTAezOFwpd84K6mvDCUX+M7wN+EEJcEGiCEGA/8FS32cE6YPqQzlw9O4/kth6h0V+5sKTzpnxmpDYPMHnomR3Os2P8msYJyC8+s389nh5unlEFBueaLTw9RFOKjjJTXnvmMDpyuYlB6HL2SzUQYdOwr0IrdWR1Oymvt3sCyhwiDDp04U64iHFHwWAr+SlkkuRv1GM/zfQrByEjKYMuCLSRGJjJ16VQ+Pf5pay9J0caRWbJCZsmXm/LaUEThfuAg8LEQIk8IsUkI8bb7sVEIcRL42D3mgaYsoqn8z+S+VFocrNrZspt9cktqSI2N8BZ480fvZDNHihpaCnani9te3sHfNx7gtpd2eCubng355dqeg84hxlN8RUFKyZGianqnRGPQ6+iXFsNedxVVz36C5HqiIIQgOsLgLWwXaQz9zt6TC+CvVpKnE9v5WPsoXHol9GLLgi2kRacxfdl0th7b2tpLUnRQgv4apZQVUsrpwCXAi0AhEON+FAEvAZdIKa+QUla05GLrc0G3BIZ1jeetXS0vCt198vb90T3JTH55LQ5nXQ/a2u8LOHi6ir/eeAEJZhN/+2jfWa/HIwqhxBRA633gaY5TUGGhxuYko5Nm9WR0ivFaOJ6y2P66qkWbDN7+zFHhuI/cYuDPQ+Q5j815bkuEtFe6x3dny4ItdIvrxpWvXcnGwxtbe0mKNoLIFhkiW9wnssU/RLZY5n78w/1cRjhzhXyLJqXcLqXMklLeIqWc5n7c4n7us/DfRvNw7fAu7M6rIDeA66Y5OF5SW2czlz/S4iJxyTPF3zy8+3UeXROiuGFEN26/pBefHCwOqxaRPwrcopAa57eEegM8loKUkqNF2rl7ufdbdI6LpKDCgpTS20An1k+vBHOE3tsXISz3kfCIQsOvmkcUfF1bisZJj00nZ34OfRL7MPONmaw7uK61l6RoRUS2iBLZYglas52ngElAT/djEppbf5/IFotFtgjJtdDu7fapg7QU0E17T7XI/Hani/zyWronNn5X7nHl5JefqWDrcLr47HAxE/p3QqcTXHNhFwDWfn92Rc/yyy2kxJgClsyuT3yUEbtTUmt3emsYedabGheJzeGirMbujc3466oWE2E4U8OoKZaCn7BBgtkjCqHVZVJopMWksXn+ZgamDOSa5dewZt+a1l6SoiUxYxDZYqfP426fo0+idVe7FYiTWXKozJKXuR9DgXhgtnvMk6GcLiRREEJ0FUL8QQjxHyHEL4QQDfo/CiEGCSE2hTJfc9IrJZpeyWa2HSxqkflPVVhwyeCums5+agz9kF9JldXB+IxkQHMx9UuNYdvBsws4F5TXhpx5BHXvyIvcO5s9aacecThVaaHSol2c/VoKPjWMIsKIKZyxFBqqQpyyFJpMijmFjfM2ckHaBcxaOYu3f3i7tZekaClqcMgsOcrnsdDn6GzgAZklV8ks2eCHJLOkXWbJN4EH0YQjKEF/3UKIfsB3wMNou5qfAPYLIa6pNzQOyAzlpM3Nxb2T+OJoaYv0TfbUDEoLEtT1HPe4dgB+cGf1DO0a731uXEYyXxwpweZoevZuUZWtQYZQY3hEoazGTlGVFb1OeJ9Lc7ugCsotVDQiCr6d1pqSfeRJTfVliLtnw8wL0kOeT3GGpKgkNszdwOguo7l51c2s+H5Fay9Jce6JAELJXilzjw1KKLd8/4fmr+ohpRwKdAfWAm8LIR4M5SQtzeheSZTX2jlY2Pz9bk9XhOa/T442YdQLCnw2ie0rqCTSqKsTjxjdK4lau5P9p5retL2k2ubN3AmFOpZClZXkaJPXreMRs1MVFm8w2l8TnWifGktNCzQ3FIXUuEiO/OUqbh7VPeT5FHWJj4xn3W3rGN99PHPensPSb5a29pIU55aNwGMiW/QINEBki+7Ao8CGUCYMpczFOOBuKWUpgJSyEJgnhNgOPCeE6CmlvD+Uk7UUw7snAPD9yfKAG8yaSqiWgk4nSDSbKKk+Iwr7T1XSLzW2zgXxgm6a1fDdyfI6FkQ4lNbYvDn+oRDjvvOvsTkorrLV2bHsEZfyWjuVFgcmvc6vJeDZ1Qzhbl7T/utPFABveQ1F04mNiGXtj9ZyzfJrmL96PnaXnR9f9OPWXpbi3HAf8BFwSGSLL4C9aFYBaPGEgcBo9/M/C2XCUCyFKKBBuoyU8j/ADcCdQohVQMsXIQpAn04xRBp17M5r/ozYUxUWDDoR0kU40WyitOaMWy+3pMab5eOhR5KZuEhDnV3E4WCxaz0NwrEUPPsKLHYXRVVWUnx2LEeb9AgBlRYHlRa7X9cRQJTxzPPhVDX1xBT8uY8UzUe0KZr3bn2PyzMu54537+D5nc+39pIU5wCZJfOA4cA84AAwBK000UxgKNr+sXnACPfYoIRiKexDiyU0SIqWUr4rhJgGvIumRq2CXicY2DmO70827UIL4HJJHC7pLfXs4XSFVvYhlEb1CWYjZe5yEi6XJL/MwhVD62qlEIKB6XFNdh+VuudPCkMUPO6eWpuT0hp7nfLfQghiIgxuUXD4zTyCuvsMQvks6o8NZCkomo8oYxTvzH6HG1feyL3v34vNaePnY37e2stStDAySzqAN9yPsyYUS+FDNGvAr1NdSvkJMAEI3afQAvRPi/E2ngmX8lo71//7Ey75v03sqWdtFFfbSI4J7QKcFH3GUiiqtmJzuujqJ2upb2oMB09XNan/gmdXcWIY7iOvKNid1NgcdeIDoMUQKix2qqyOgP0ZfO/0w7m+K0vh3BJpiOTtW97m+oHXc/+H9/PUp0+19pIU7YxQLIWngJU0IiBSyt1CiBFAaPWUW4CMTjGs3HmC8lq73x25jbF0+1G+cbtznt2wn4XzzpSjLq2xhXwBTjCbvJZCfpkWoE6PbygK/VJj3EFfW4Pic8HwWAqhChXg7ZRmsTuptjobiEJspIEqiwOrw+m3cB3UtQ7CucDrG9nRrGgZTHoTK25cwW3/vY2H1j+E1WHldxN+19rLUrQQIltcDMwBTMDLMkt+KbLFBOARoA9a/5vHZZYMaadjKGUuKqWUu6WUtUHGFUopG3agOUf06aSVrz4cprUgpWTFzuNc1i+FezL7sHHvae+FHaC8JnSRSTQbKa3Rdg7nlWkfV5eEhqEWT1/lg6fDt2w8lkhCGMLn6ZRWbXVSa2/YY9njPrLaXQH3IPhaB+GIgnIftQ5GvZHXZr3GbRfcxu83/56szVnntDOg4twgssUUYBtas7NLgK0iW8xEyxB1ACvQbv7fc4tHUJp8/yaEGCCEaDNFa/q4a/kcLgyvfPWx4hqOl9Ry+eA0Mvt1wumSfH28zHs8HEsh0WzC6ZJUWBzencP+LIFeydpac0vCL7Xd2K7jQBj1Ar1OeK2MaFNDS6HSasfqcAXcJa1vsvvI/V9V9O6cY9AZWHTtIm4ffjuPbH2E/934v0oYOh5/RIvp9pZZ8kLg98DrwJsyS06VWfIhmSUvQROJ34cy4dn+UtvM7Z/Hd3+itFGDpgHb3eWsL+mbwgXdExACvsrVRMHlkpTX2r3lGILhGVdWY6PMe0ffUFDS4yPR6wTHS8JbK9DoruNACCGIMuq9Be3MEXUv/LGRxqDuIyGa5j7yjFXVsVsHvU7PS9e8xE9G/oQnPnmCX370SyUMHYthaC4jz27YxWjFSl+rN24RWpZSUEK/svinzXy7Io16OsVGeN02obI7r5zYCAN9UqIRQpDRKcab2lppceCSWqwgFDwX6mqrk9IaGzERhgbZTKA1lemSEEluEwrjVVrs6HUirA1koH0+njIVfi0Fi4PYSP/rhXqB5iZkH4XzGkXzohM6/j3j35j0Jp757BlsThvPXfkcOqGstw6AEfCtwulJwaxf96cY6BTKhGcrCm2KLglRnAxTFPbmVzIwPdZ7J9wr2cyJUu1iXVarfdah+u89/RZqbI6gsYgeSWaOlzZFFLQMoXA3fUUadd7MpQYxBbcomAy6gJZCnZTUJmQf6VX2UasihODZK57FpDfx1PansDltPD/zeSUM7Z8TwADcWwZklnSKbHErcKjeuB5obQ+C0qFEoVtCFD/kh76BTUrJvoJKrruo65k5Es1sP1SMlNIb1E2MDk0UvL2MbZql0NjruiWY2bTvdMhr9VDlvqMPlyij3ts5rX72UYzJgM3posrqCBhT8BWhcILGKtDcdhBC8OTlTxJhiODPH/8Zm9PGy9e8jF7XqtnkirMjh3p7xGSW9FcE63ogpBYHHUoUuiREsuGHU0gpQ7qTLqqyUWl1eIPUoFUyrXZv8mqsFpA/PJZCrc1BaY3dbzzBQ1p8JEVVVuxOV1iN6yssDmJDXI8vUSY9B077txQ8PZKrrY5GLIWmpqS6X6NEoU0ghOCxyY9h0pvIysnC7rKz+LrFGHQd6lJw3iCz5L0hDn0aOB7KwA71TUiNjcTqcFFpdYR0Ife4ibonnilY5ylel1tSQ41NC+rWv7MOhOdiW23Veh13a6QHQ3p8JFJCYaU15A5qoMUUYkNcjy++9Yrqvx9Pj2SXpJGYwpl/h+MJUu6jtskfM/+ISW/itxt/i81p4/VZr2PUh3+zoWgfyCy5LdSxHUoUUmK1O/OiSmuIoqDFH3xbbXpKSRdWWqm2ahm39e+sA+EbUwiWynqmKY8lTFFwkB5GLwUPvqJQ//34WiqB3Ee+1kE4F3jlPmq7/ObS3xChj+DBjx7E7rSz4sYVRBjC20ypaJuIbDEA2COzZNi+wQ6TkgqQHK19oeu3xNx2oIgnP9xLra3utgpPoNf3jt5zIS+tsVFj94hCaNrpG1OoDOL799eUJxQqrYGL1jVGlM+mtPrvx+CTLxp489rZpaSqMhdtkwfGPcA/r/wn7+x7h1krZ2FxhPd9VLRpmvSjOxtLIR+46yxe3+x4SkJ78vFB64cw75UduKTWWvN3M85U4jhZWkui2VjHneKpPlpafUZYQrUUIg1axdHyWjtOl2w0bdTXUgiHKovDWwo7HHwthfpxg7qWQjO7j1SZizbPfRffh1Fv5Cfv/YSr37b8Yt4AACAASURBVLiad2a/g9nYeE9yRbugSVsGmvxTlVJWSClfburrWwKP+6jYRxTW/3AKl9RSTVd/nYfTpztbYaWV1Ni6rphokx6TXkdpjZ1qt2UR6p4AnXv/gEdQohoRkwSzEYNO1BGwUNDKVIQvCo0Fio0+lkKgmILv68NJh/VaCsp91Ka5e+TdvHLtK2w8vJEZr8+gytb8DasU7YMOdf+WZDYhBBT6uI9y9hXSPSmKX0ztT2Gllb0FZ1JW/VVAFUKQYDZSWm2j1ubAbNKHdUEzmwwUe3sZBxYFIQTJMaY6AlYfh9PFcxsP8PEBLb1YSonF7iIywIW7MepuPqt7LJSYQlOb4XimDqcHg6J1WDB8ActmLWPrsa1csewKKqzN359E0fbpUKJg0OtINNe90O7Jq2Bkj0Rvl7Mf8s/0MSiqstbpQuZBK4Fto9rWsHhcMKIj9N7zB7MwUmIivBvK/PH+d/k8vX4/c1/+nNJqG1Z3X+fGxCYQ+kZiAgZdKO6jJoqCyj5qV8wZNoflNyxnx8kdTFs6jTJLWfAXKToUHUoUAOLcu3MBqqwOTpbV0i8tlt4p0UQadXU2txVX+e+VoHVQs1FjdYTtqjGbDN5yEpEBgrYekmMiKKoOLAqrdp7w/nvbwSIs7sB3OO0wPfhaB/Uv0CaDT6A5hB3N4eCxMFTbzfbDTUNu4s2b3mRX/i6mLJlCSW1Jay9JcQ7pcKIQ624YA3DIXZq6b2oMep2gf1qst+OZxe6kyurwaykkRmslsGuaYCmYTXqv+yiopRAd2H3kdEm+yi3lR2N6EBtp4LPDxVjsrpDm9YfvnX7967OvpRAoptDUi3qbKY6lCItrB17L6tmr2X16N5MWT6KwOqQKCYoOQIcThbioM5bCkSKtNHWGe8dyt8Qob8E8T4A3xY+lYDYZqLE6miQKJr3Oe/5gd/RaTMG/pXCiykW1zcnFvZPolRzNidJaH0uh6TEFnWh4gQ8lpnC27h9lKLQ/rup3Fe/e+i77i/czcfFECqoKWntJivBo0q+uw4lCbITRW57Cswegs7v7Wee4KPLLLUgpvS6epOiGlkKkUYfF4aLaT+vKYPjm/AcXhQhq7U6qrY4Gx05UalbB0K7xdEmIJK+sFouj6e4jT/aQv9iAMZR9Ch3um6IIhWkZ0/hgzgccLTtK5qJMTlacbO0lKUKjyVsGOtxP3ddSOF1pJcqoJ9p9t58eH0mNzUml1eEdE+cn5z/KqMdid1Jrc4btqvFN3Qx2R++pvlrmFjFfCqolep2ge6LZW/3Vs/muKZaCRwv8ZVKFtk9B3eqfr0zqPYl1t60jvzKfzEWZ5JbntvaSFEGQWbJCZjVty0DHEwWfmMLpSiupcRFed4lnF3FBucUrCv42gkUa9e4m985G9xr4w9fNEkxQfJvy1OdUjYtuiVGYDDq6JkRRY3N6LZ/IAC6eUNblLzPU17oJFFhXonB+c2mPS/lo7kcU1RSRuSiTI6VHWntJihaiw4lCbKSRGpsTu9PF6QoLqT7tMD39kvPKaqlyu2xiIxrWSIo06pESamzOsCqYQn1LofGLd7y7imq5H0vhVI30tu1Md7u/jhRpZTmakpKqa8R9ZPJ5j4GETImCYmy3sWyct5FySzkTFk3gYMnB1l6SogXoUAXxQHMfgVYOorDSyqD0OO8xT22k0hobVW5rIpClAFBltYe96cr3rjuYpeBpwlNe01AUimpdTHAX6vOMKwpx/4M/dI3sFzCEIAqqTIUCYGSXkWyev5mpS6cy4dUJbJq/iYEpA1t7Wec9IltkAFcAA4FE99OlwF7gQ5kl6zfdCUiH+6l7eg1UWOwUVlnp5GMpeJrdV9Q6vJZCjJ9Assdnb7G76lzkQ8G3QX0wS8HjPqpvKVjsTqrtZ9xdnkJ7HlFoWvaR9l9/N/y+whdpat6UVFQ/4A7HhZ0vJGd+Di7pInNRJt+f/r61l9SxMWMQ2WKnz+NuzyGRLaJEtlgC7AOeAiYBPd2PScBfgX0iWywW2SKk8sodzlKIcV9AKy3ahd83kOypLlpRa3d3GdP5zcv39dkbwky78b3ABgraeogPEGj2xA48ri+PcHnSV88m+8hfCWvfz8AUwCRoakqqRxJE2yqoqzhLhqQOIWdBDpMXT2bioolsmLeB4Z1D6guvCJcaHDJLjgpw9ElgGnArsFpmyToXE5EtjMC1wD/cY38e7HQdzlLw+NvLauxIWdc9ZNTrMJv0VFjsVFoDl7b2DS6H2wfAMz7CoAtaM8ls0mPUC8rquY8Kyj2ptJqwe97DGUuh6e4jf7EBQwjF7s42JVWFJDoeA1MGsvX2rZiNZiYvnszOvJ2tvaTzkdnAAzJLrqovCAAyS9pllnwTeBBNOILS4UTBc5fvuYDG1Askx0UaNfeRxeHXdQR13TPhuo8MPqIQDCEE8VHGBu6jU5Xa2tPiPO4jjyiEVj7DH40Fio0hrlWhqE/fpL5sWbCF+Mh4piyZwmcnQmoDrGg+ItBiB8Eoc48NSocTBc/mK48oePzxHuKiDJS73UeB+hL43omHG2j2WAqBykXUJy7KSHlt3ZTU0273UZq7rHe0qZ6l0JSU1EaWYwzBDFAF7RSB6J3Ymy0LttDJ3InLl17OttyQOz8qzp6NwGMiW/QINEBki+7Ao8CG/2/vzMOkqM7F/X7d0z0LILigRlRAQTQuIV73RBhBwKAoBkRQBP2JGveoiVuMTQPR64bXJfdRcjUsCooEl6gIggzo1YjGuNy4BEFQxAgoKgPM9EzP+f1RVT09PdXd1dU9U901532eema66lTV9/Vyvjrfco6TC/oupmB1mNb8Q6kuoq6VRh1DY1xlGCkku4/cxRScprJ2KS+jtr7linBbamMEpTmTKhgQqsJBM0VWXK1NkOlJP+RgNKRTUjWZ2L/r/qy8YCWDZg1i2GPDeH7c85zU+ySvxeoIXA4sAdZIVN7CyDayprbtipGNdLS5/wonF/TtSOGbTO6jugZjCos0hVrJT+KhnEcKxv2dup06lZe1mubiux0xOoelRUduGTDXU1hn0MNJ3ERPc6HJxj5d9mHF+Svo3a03w+cOZ8maJV6L5HtURG0E+gMTgNXAocBp5nYY8Kl57EizbVb8N1Iwn/KtTJ3W7qMQqzfVps08gpRAc64xBbO9E5cMGJ39t9t3tNj37fYYnUOt223aVu/aKGTq953EC9zeV2ekdiz26rwXyycuZ8icIYyYN4KFYxZy6kGnei2Wr1ER1QjMM7e8cWwURLIURyjnxRFtiRXgtdYpSK1Y7lxexra6BgKVobQunhaBZpcxBafuI0Oe1JFCA51DLe9rBZvdenHydf/ke752PnUcunfqzisTX2HonKGc+eSZzD9rPiMPHum1WB0OiUoAOA74QEXUtmztLbIaBRGpBB4GzgEagDU0R7v7AxcC94nI48AlSqm6pHMvBi4GKCsro6amxqlcLaitrXV87s5G49F0w+bvAXjv72+yNtzcJW36dz076xuRpka+3RKzvW5trPnxdt3atdQ0feFY1g1fGMaobud2RzJ//009321vbNF2w5YddC9varGvcacx5XdTPO7qfVy7zshwijXY62yR7tjqrfGsbexYv954Pz5b9xk1NZln2Mzlc/YLftZ58gGTub72ekbPH80th9xCdfdqwN86p8MjnbsArwLV5l9HOBkptCyOUCnFEZK+OEIpNQOYAdCpUydVXV3tVK4W1NTU4PTchngTLF3E9rgAiqGDBrRYI+DNuo955Yu1BMrC7NdjL6qrD291jR2xRnhlMQAH9+tL9fG9HMv6buO/YM1quu3Sherqn2dt/2bdx6z8ci0DBw5MuHFiry2la2W8hc6Prl3FR99uJhQqc/xeJLP+9XXw8T8Jh8L257/0AkDaa+/y+VZ48/WMbex4u/4TWPspvXv1prq6b8a2uXzOfsHvOg84cQCnzj2VqR9NpW+/vpx7xLm+19mOttJZojI/w+EQxiB9ikRlM4CKqDHZrunEKIwFrlJKPWV30DQSC0zjcD8OKubaklAwQDAg1DU0EQ4GWi0aEw4GaIgr6hvjaWsJkqt63VY0O83r71xeZsrTZE7Ep4xAc/eWH01imoqcpGl9fjr2262SoT/eO+1xnZKqccMu5buw6NxFnD7vdM57+jwamhroRS+vxfITo4FNwEc2x6xOpFsuF3RiFApeHNHWlJcF2BGL2waSrX3b6xvTBprLgvnEFIxznfahVlbR9vpGKkJBausbaWxSpC4Il6hIdpGO6uS8V68flPl819Nc6EhzR6dzuDPPn/M8I58YyQXPXsC1fa+lmmqvxfIL1wAR4HPgRhVRX1kHJCrdgG+Bq1VErXR6QSePwUZxhGQojpDciiPaGisDyS7V0hodNCln+fm5TnORGCk4bN9sFAyfvRV07lTW8gqWGO6zj/IMNLtMSd3fnOl1390q87q/prSpClXx3LjnGN53ONNXT+fBVQ96LZIvUBF1H9API977sUTlZomK9XDu6onMyUihuThCClMc0dZYHb9dp99y8rfslcG5z5Kafo4hO6ysom31RqjGmr21oiz1/NyMTSu5PMo+GnPUfuy7axUnHLh7XvfXlD4VZRUsHLOQwQ8N5spFVxKLx7j2+Gu9FqvkURG1GZgkUZmB4cKfJFH5LS4f0rMaBaXURhHpD5yFkZJ6KK1TUh8AFiilWi827AGZRgrJ8YJQq463NTnHFIJWTMFZe6vi2hopWEahMuWTaZ762l3nnG9IwK1REBF+1meP/G6u8Q3lZeVM/vFkZnwzg+uWXEd9Yz03nXiT12L5AhVRq4DjJCr/D/gjsB4XowVHdQpmZ1+w4oi2xhop2HXoyfUD6aaJTsbtLKlOp4q2DNjOBtMo1FlGIdV9lH45zVzkcoteZEdTKMoCZcwdNZdQMMTNr9xMLB7j1oG36kkXC4SKqEclKguA3wEbgS25nO+7imZonj7bzvWT7D5yMpOpk7hDMs3ZR87aWyud7YwZRmF7GveRZd/c/m7yNQr6B6spJGWBMmaPnE04GGbyisnE4jGmDZqmv2cFQkXUD8ANbs51ZBRE5BiM4rUw8IhS6u8iMgCYAhwAfAbcppRa7EaIQmN19tkWlHFSdZz7SCG37KPmVd7MQLNlFFLCHdbIIx83Tj7olFRNoQkGgjxy+iOEA2Fue+026uP13DXkLm0YCoBEpR/woYqonKdUdlLRPBhYBHyFEWCeKCJnA08Cb5h/TwCeF5GfKaVW5SpEoWl2H2UJNDsYKbiuU3DoPrLmWbKMwvZ6e/eR9TtxPSGex9NcaDR2BCTAQ6c9RDgY5p437iEWj3HfKfdpw1AYXL2JTkYKtwLPAWOUUk0icg0wFyOwPDFxd5HngFuA090IUkisWIFdh14ezG2k4Dr7yKEtsWZkTY0ppC71kP/cRXmdrldO07QZIsL9v7ifcDDM9L9NJxaP8d+n/jcB0YGsPGmzlNTDgXOVUk3m61nAPcDjKe1mAv/lRohCY3Xk2WIKzkYKbusUch0pGG9vbcxYOzr1vok6BZe/E7dFbxb5xiQ0mkyICHcPvZvysnJuf+12YvEYfxrxJ4KB3BeU0uSHE6MQApKXBvve/Jsa0f4G6F4IofIlFEzvPmqRfeTAKLjOPnJ4muXqSh4p2C3+I4nsI4+K1/RQQdPGiAh/GPQHwsEw0RVRYvEYM0fOpCzgy3yYosXJu70Bo2JuGYBSKi4i4zBmS01mf2BzYcVzRyiD+6hl8ZqT7CO3dQrOOlERoSIUoD4ppmC3TKh1OdfFa3mOxPUiO5r2QESYXD2ZcDDM7175HQ1NDTx25mOEgqHsJ2sKghOjUINRsZxAKfWkTbszgaJYtdtKI82WfZRppCBiLBCT60hBXFQeV4aCiZHC9ljcTFNtatEmkOdIId/AnR4paNqTm0+8mfJgOb95+Tc0xBt4YvQThIPh7Cdq8sZJRfOlDq81HXC+8EAbkhgp2MUUHAaagyI0KpVznYI1AVwufWhFKJioU6hriJtxhlSjQM7XTUZnH2lKjetOuI5wMMxVL13FL5/8JQvGLKCirMJrsXxPwZwCSqnXlFLrC3W9fMgUUyh36D6yArPBHP0m1vKTuXSilaEgdY2GEahvaLItqkuMQDyKKeg6BY0XXHnslTx06kO8sPoFznjiDHY27PRapFLC1Y/WtVEQkX4iEs/esv1pdh9lmeYig/vIsie5Zh81mUYhl7PKk0cKjfHE1Bct5Am0lCtX8o0J6OxAjVdcctQlPHr6o7y85mVOnXsq22PbvRapFPgKuMjNifmG9Yvy8dFaDyH7LKmZjEL6tNZMKJW7+6gyFKC+sdl9VFHW2ih4nX2kRwoaL7ngpxcQCoaY+MxEfvH4L3jhnBfoUt7Fa7GKFnOai0fcnJvv819RrqBijQayBZq72GT5WFidYK6BZusNycXN0zKm0JSY+iKZfMsELD3cfmA6pqDxmvFHjGfuL+fy+hevM/SxoXxf9332kzQ548sE4FCGHtRyB1WFg+zaKX02gyTcR7nGFMyRQg7nVIaC/FBnrKdQ12DvPsp37qN8jYpOSdUUA2cfdjbhYJizF5zNyXNOZvH4xexWuZvXYnmOROVAjKUNDqb10gYvqYhKLSFIiy9/6iFzNKBsHotFhAW/Op43bhyc8RrNU2DnhrUmdNdK53nVySMFa63mVPKuaNbZRxqfcOYhZ7Lw7IW8//X7DJ49mC07cpoZ2ldIVColKrOBT4C7gZOAnuZ2EnAX8IlEZZZExVHqlj9HCqb7KN36wEf1yv5kMeCg7jz77kZHVc/JDDyoOzcPP5hxx6RdvbQV4bIADXFD1rqGuH32keQ2fUYq+U5ToY2Cppg47aDTeG7sc4x8ciTVM6tZNmEZe3Xey2ux2oYqyiQqbyftmaEiaob5/53AUGAc8IyKqIbkUyUqIeAMjIXQ7gSuynY7nxoF03+eR8TjztFHcPXgvonlMp0SCAgXDzgwp3NCQaEh3oRSivrGpsR6EC2um+ciO15PqKfRFJphfYbxwjkvMGLeCKpnGYZhny77eC1W4dlBo4qoo9IcHQtcpSLqKbuDppFYYBqH+3FgFPzpPgqmdx85pbwsyAHdOxdIosyEggEa4k3Um7UKdoHmxDQXHmUf6amMNcXIoN6DeOncl9jwwwYGzhzIF98XRf1se1KOETvIxndm26zkaxSKsqewgslN+ViFdiQUDBBrbEqsqWCXkppvRbMOFGv8yok9T2TJ+CVs2r6JgTMHsu67dV6L1J4sA6ZJVNL6qyUq+wFTgaVOLpiP+8h1cURbY8UBSsMkWO4jlZg+uyIUhMaWbfKd+0jXGWj8zPH7Hc/S85Yy9LGhDPjzAJZPXM6Bu+Xmxi1RLgeWAGskKm9hZBt9Zx7ripGNdLS5/wonF3T9/KiU+kEp5ao4oq2x0khLZKCQcB8lRgq27iN32VCp52s0fuXoHkezfOJydjTsYMDMAXyy5ROvRWpzVERtBPoDE4DVwKHAaeZ2GPCpeexIs21WfBlobq5CLg2rEAoGaGxSiZlSbesU8l2OswCR4jtHH8FP9+uW93U0mrai/979qTm/hsGzBzNw5kCWTVjGoXse6rVYbYqKqEZgnrnljS89zZarpFRGCpa7q9Zcn9kuJTXvmEIBBgpjjtqPvnvpqQU0xc1hex5GzcQaAhKgelY17/37Pa9FKil8aRSsjrNEbEIihXabWdVsX7yW24pu6c7XaDoCh3Q/hBXnr6CirIJBswfxzlfveC1SmyBRaTUtg0Rlf4nKdIlKjURluUTlLonKvk6v6W+jUCJDBSuFtrbecB9lKl5zPc2FLjTQdDD67t6XleevpEu4C4NmDeLNDW96LVJbsFOicoz1QqJyOPAecAFQa26TgH9IVA5yckF/GgWslFSPBXGINavrzlhji9fJWF26V4vsaDSlSO9de7Pi/BXsXrU7Q+YM4X8//1+vRSo0qT/su4CNwMEqok5TETUCOATYAkxzckFfGgVKzH0UNt1H1vxHdms46IpkjcYdPbv1ZOX5K/lRlx8x7LFh1Kyr8VqktuRE4A8qor62dqiI+jfwnxhzIWXFl0YhkXtUYu6jHWb2kd0yofl26jolVdOR6bFLD1acv4Ke3Xoy/PHhLF3rqI6rVEjt6OxWwFwPOMoS8adRKLEO0DICdeZIwS59NN+YQIm9JRpNwdm7894sn7icPrv14bS5p7Fo9SKvRSoUt0tU5ktU5gMNQB+bNj2Bb5xczJdG4YA9OgFw8iGlMWtiYqQQs0YKhe/BtU3QaGDPTnuyfOJyDt3zUEY+OZLnPnnOa5HyZSUQBLqb2z+AXjbtzjKPZcWXxWv77VbF+5OH0iXHGU69IlxmdNmW+8gu0KxTSjWawrB71e4sm7CMYY8NY9T8UcwbNY/RPx7ttViuUBFV7bDpTXTkkQLALhWhknEjWdNy1GUMNBfmXoWobNZoSp1uFd14+byXOabHMYxdMJZ5HxSkGLhoURH1gdNpLnxrFEoJy31kTXORKfvIbex8t05hLqs+kLmTjnV3AY3GZ+xSvguLxy/m5/v/nPFPj2fWu7O8FqlgSFT6SVTibs7VRqEISLiPYundR/kOekSE6085WE9TodEk0TncmRfPfZFBvQdxwbMX8D/v/I/XIhUSV72GNgpFgJORguQ5zYVGo7GnKlTFX8f9lVP6nMJFf72IP676o9ciFQpXfgVtFIqAhFFIjBTSxxRKpPRCoykpKsoqePrspzm93+lcsegK7n3jXq9F8gxtFIqA5pRUY5qLkM0yaTr7SKNpW8rLynnqrKcYdcgorl1yLXe8dofXInmCNgpFgFWXUNfQhIh9oZq2CRpN2xMOhnli9BOMO2wcNy67kakrpnotUrtTGon8Pic5pmA3SoDSq9LWaEqVskAZc86cQygY4taaW4nFY0w5aUqH+Q06NgoiciBwCsaan7uau7dirP35klJqTeHF6xgku4/s4gmgJ7TTaNqTYCDIn8/4M+FAmGmvTqM+Xs8dJ9/RIQxDVqMgIpXAw8A5GPNqrMEwBmCsDXohcJ+IPA5copSqayNZfUvYmvuooYkuFfYfiY4paDTtS0ACPDziYcLBMHe9fhexeIx7h91bSobBlaBORgp3AkOBccAzSqmGFncVCQFnAA+Yba9KOnYxcDFAWVkZNTU1bmSktrbW9bmlwM7GpJSieCM1NTWtdP7XF8bbvnXrVt++F37/nO3QOhc/o6tGs6nHJu578z4+++Izru5zNQHJLRzrgc5fARe5OlMplXEDNgPjHLQbB2xOd7yqqkq5Zfny5a7PLQVq6xpUzxueVz1veF4dPe1lpVRrnZ9c9bnqecPzatyMNzyQsH3w++dsh9a5NGhqalI3vHyDYjLqwmcvVI3xxpzOz0dnYLvK0v8WcnMyUiin2V2Uie/MtpocSXYN2RWugc4+0mi8RES4ffDthINhpq6cSiwe49EzHqUs4L9cHScaLQOmiciHSqnP7RqIyH7AVMBXK1e0F8kdvt0UF5D/3EcajSY/RIQpJ00hHAzz++W/p6GpgdkjZxMKhrwWraA4MQqXA0uANSLyFka20Xfmsa4Y2UhHm/uvaAsh/U5Lo6BHChpNMXPLgFsoD5Zz/dLricVjzBs1j3Aw7KlMEs2SHRpxnh2a1SgopTaKSH+MRRpOAQ61uekDwAKlVKPTG2uaceI+Cui5jzSaouG3P/st4WCYXy/+NaPnj+aps56ivKz9vecSdZgdGjWzQyPZs0MdOcTMzn6euWkKTEujkK54zfir3UcaTXFw9XFXEw6GuezFyxj55EgWjllIZaiy8Deqokyi8nbSnhkqomaY/7fMDo2kZIdG02eHpiOvKImIBIDjgA+UUtvyuVZHJnlwkG4pTl2noNEUH5cefSnhYJiL/noRI+aN4Nmxz9Ip3KmwN9lBo4qoo9IcHQtcpSLqKbuDppFYYBqH+3FgFPKd+6gL8CrGMEXjkuRimHQro2mboNEUJxceeSGzRs5i+brlDJ87nG317fp8XPDsUCcVzfMzHA5hVM1NEZHNAEqpMU5urGmJiOEaypZ9pNFoio/zfnIeoWCI8QvHM+yxYSw6dxFdK7q2x62N7NCofKgiabJDo7llhzpxH40GNgEfZTi/m5ObadITECGuVAb3UTsLpNFocmLsYWMJBUKM/ctYhswZwuLxi9m1ctfsJ+ZHc3ZotDDZoU7cR9cAYeBz4Byl1EnWBoww21ydtE/jAqvTD+pZUjWakmXUj0excMxC3vv6PQbPHsyWHVva9H4qojZiuO8nAKsxskNPM7fDgE/NY0eabbPiJCX1PhGZC9wOfCwidwD3KKXqcbncm6Y1ggCKULqYQvuKo9FoXDKinxFwHvnESAbNGsTSCW1b06sihc0OdRRoVkptVkpNAoYApwMficioQgigMbAGAumnztZmQaMpFU7pcwovnPMCn377KdUzq/k29m27yyBR6SxRqcr1vJyyj5RSq5RSxwHTgD9i+LL0aKEAWJ1+2kCzXiNPoykpBh8wmEXnLuLgPQ6mc1nnNrmHRGWoROWMlH2/kqisB74HtklU/iVRGev0mq66GqXUo8BBQA3wHNC2jrMOgOU1Sj8hnjn3kbbBGk3JMLDXQBaevZBwoM2mwbgDONB6IVG5EXgQI9NoAjAReB2YI1FxNJW26+I1pdQPwA1uz9e0xOr009YptKcwGo2mVDgIeDfp9eXANBVRk5P2PSZR+RL4LfCnbBd07ZQQkX4iEnd7vqYlVsggXewgMfeRNg8ajaaZOiC5hHovYLlNu+XA/k4umK+nWvdQBcLq9NPVIwS0+0ij0bRmCfCrpNerMOZCSmUoxmR5Wcl3hQjdQxWIQNaRQjsKo9FoSoUbgDckKiswkn8eAB6SqPTCiPkKMAijCHmCkwv6b9mgEsWKKQR076/RaByiIupzicqxwN3AHIw+XTBmTR1nNvsnMFpF+TiFsAAAB/hJREFU1DNOrqmNQpHQPFLwVg6NRlNaqIjaAIyVqOwCHAnsjWEYtgIfqYhan8v1tFEoEhIjhXRFatpYaDSaDKiI+gHDZZQXuiSqSMgWU7DQi+xoNJpsSFT6SdRddqgol72MiPQDPlRKBR22bwJ2urqZMaLpaEt9ap07BlrnjkE+OlcqpXJ6gJeo9MNwHeX84J+v+8ixUyNXpVrcRORtpdKuPORLtM4dA61zx8AjnV098efjPvoKcFQ2rdFoNJrSIN9pLh4poCwajUaj8ZhSCTTP8FoAD9A6dwy0zh2DktHZdaBZo9FoNMWJGWj+UEWcJQIlUyojBY1Go9G0A9ooaDQajT9xVfKqjYJGo9H4D9fZoUVpFEQkICLXiMjHIlInIl+IyD0i0in72cWDiNwkIk+JyFoRUSKyLkv7Y0VkqYhsE5EfROQlEemfpu0+IjJbRDaLyE4ReVtEzmoTRRwiIgeJyBQR+Zsp1zYReVdEfmf32ZlrcjwjIltFZLuIvCoig9Jcu6uIPCAiX5rfiX+KyKUi3i5eberwuIh8JCLfi8gO83s7XUR+lKZ9Setsh4hUJX3PH7Q5XvJ6m7rZbbU2bT3VV0XUDyqi3GWHKqWKbgPuwyi8WIhh7aYDDcArQMBr+XLQQwHfAC8D3wLrMrQ9DmPBjDXANea2BtgGHJ7SdjdgLVALTAEuxpjzRAEXeKjvf5ryPg5ciTHP+5OmXO9hVGZabQ8035uvgZuAy4B/mJ/zySnXDWPME99gfhcuMr8bCpjs8Wc82Pxe3mbqcDHG9MW1wEZgT7/pnOZ9uNv87BXwYMoxX+htyrASGJ+yne0nfT3/Mtm88YcCTcBfUvZfab5R53gtYw66HJD0//+R2SisAn4AeiTt62HuW5LS9k7zvRiRtC9oXuMboLNH+h4FdLXZP82U94qkffOBONA/aV9nYD3wCWZmnLn/MvP8K1Ou+xcgBvT0+rO20fksU+br/a4zxsycjcC1aYyCL/Q25ZrpoF1J6+v5F8rmDbU6kBNT9lcA24EXvZbRpV5pjQLQx9T5EZtjj2AYyb2T9m0APrVpe555nTFe65si1+GmXA+ZrzthjIqW2bT9vdn2mKR9r5mffUVK2xNTO95i2YBjTNlu97POGA8jfweeB3qlGgU/6W0ZBYyne9sHLz/oW4wxhaMxOsFVyTuVUnUYC1Qf7YVQbYyl0xs2x/6GkUXwHwCmn7qHud+ubfL1ioV9zb9fm3+PAMpJry+YOohIAONJ9B/mdyCZVRg/HM/1FZEKEdlDRPYVkaHAw+ahF82/vtPZ5BrgYOCKNMf9pvdoYAewTUQ2mbGArknHS17fYlxPYR9gi1Kq3ubYl8AJIhJWSsXaWa62ZB/z75c2x6x9PVy09RwRCWI8ITUCc83dueiwK1Bp11YpVS8iWygOfSdhxBIs1gHjlVKvmq99p7OI9AaiwBSl1DoR6WXTzE96rwKeAj4FdgGGYxjDgSJyglKqFh/oW4xGoQqwMwhgDMusNn4yClXmXzu961La5NK2GPgv4HjgZqXUJ+a+QulrtS8GfZ8BPsbwHf8UOB3YI+m4H3V+CCPhYXqGNr7RWyl1bMqu2SLyPvAH4Grzb8nrW4xGYQewZ5pjFUlt/ISlT7nNsVSdc2nrKSIyFeNJaoZS6vakQ4XS12rvub5KqQ0YsR6AZ0TkL8BbIlJl6u4rnUVkPDAEGKCUasjQ1Fd623AXEAFOxTAKJa9vMcYUNgJ7iIjdG9UDw7Xkp1ECGDqD/VDR2veli7aeISKTgVuAP2OkpiaTiw5bMRZnatXW/I7sQRHom4pS6n2MNMTLzF2+0dmUYTpGvOTfItJHRPoAPc0mXc193fCR3naYBnEjzaPCkte3GI3CWxhyHZO8U0QqgP7A214I1ca8Zf493ubYcRgBp78DKKW+wviiHJemLXj8HpkGIQLMAiYpM50iiQ8whszp9AVTB6VUE/AO8FObB4VjMILwxfqdqMSoKQF/6VwJdMd4Ol6dtNWYx8ebryfhL71bYfZL+9KcRFH6+nqV3pUh7etwMtcpjPdaRpd6ZatTeAujJmGfpH37mPuWprS9i/R1CluBLh7qeasp22wyFBpiBOziwE+S9lm53P+iZS735aTP5W4Aenmo795p9p9k6rcsaZ9fdA5hZOGkbpeaMi8yXx/kF72B3dPst36LyfUoJa2vJ18qBx/AAzRXNE8C7jHfoJpMHU2xbRh1A7eY29dmh229Pi+l7QkYTxhrgF+b2xqMytifpLTdHSO7ZRtG9sfFwHLzPbvQQ32tL/h6YAKtKz+HJLXtg1Hl/TVwI81Vn43AsJTrhjGemBrM78Ikmqs+p3r8GT+NkWp4G3AJRsBxNkYixHe0LGDyhc4Z3ote2BevlbzewL0Yaaa3YbhDf4NRya7Mzz+5Wr+k9fX8i5TmAwgC12FU/9VjuEum41Glbh561Jgfrt1WY9P+eGAZhiHYBiwGjkxz7R7AHGALRpbCO6SU23ug78wM+rbSGTgEeNbsPHdgFPKcnOba3YAHMXy29cCHGEFsaWu9sug8BqNw6wvzc9iJkYX0ALC/TfuS1znDe9ELG6PgB72BM8zf45fm57wdo27qZlIKz0pdX73Ijkaj0WgSFGOgWaPRaDQeoY2CRqPRaBJoo6DRaDSaBNooaDQajSaBNgoajUajSaCNgkaj0WgSaKOg0Wg0mgTaKGg0Go0mgTYKGo1Go0nw/wG4Bc5vp0Ab+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc204704490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    fig = plt.figure()\n",
    "    input_ = i # 0-3\n",
    "    weights_ = np.hstack(weights[input_])\n",
    "    weights_ = np.reshape(weights_,[31,1])\n",
    "    weights_ = np.concatenate([np.hstack(np.flip(weights_[1:,:],0)),np.hstack(weights_)])\n",
    "\n",
    "    ax1 = fig.add_subplot(111)\n",
    "    w,freq_res=signal.freqz(weights_)\n",
    "    \n",
    "    plt.plot(w/np.pi*500,10*np.log10(abs(freq_res)/max(abs(freq_res))))\n",
    "    plt.tick_params(\n",
    "        axis='x',          # changes apply to the x-axis\n",
    "        which='both',      # both major and minor ticks are affected\n",
    "        labelsize=18.)\n",
    "#         bottom='off',      # ticks along the bottom edge are off\n",
    "#         top='off',         # ticks along the top edge are off\n",
    "#         labelbottom='off') # labels along the bottom edge are off\n",
    "    plt.grid()\n",
    "#     plt.xlabel('Frequency (Hz)')\n",
    "#     plt.ylabel('Amplitude (dB)')\n",
    "    \n",
    "    plt.xticks(range(0,501,100))\n",
    "    plt.xlim(0,501)\n",
    "    plt.ylim(-45,0)\n",
    "    plt.yticks([0,-10,-20,-30,-40])\n",
    "    plt.tick_params(\n",
    "        axis='y',          # changes apply to the x-axis\n",
    "        which='both',      # both major and minor ticks are affected\n",
    "        rotation=90,\n",
    "        pad=0,\n",
    "        labelsize=15.)\n",
    "    \n",
    "    angles = np.unwrap(np.angle(freq_res))\n",
    "    ax2 = ax1.twinx()\n",
    "    plt.plot(w/np.pi*500, angles, 'g')\n",
    "#     plt.ylabel('Angle (radians)', color='g')\n",
    "#     plt.xscale('log')\n",
    "#     plt.title('Filter of input branch ' + np.str(input_))\n",
    "#     plt.hold\n",
    "#     plt.ylim(0,20)\n",
    "    plt.tick_params(\n",
    "        axis='y',          # changes apply to the x-axis\n",
    "        which='both',      # both major and minor ticks are affected\n",
    "        labelsize=15.,\n",
    "        rotation=90,\n",
    "        colors='g')\n",
    "#     plt.ylabel('Angle (radians)', color='g')\n",
    "#     plt.yticks([0,5,-5,-10,-15])\n",
    "    plt.axis('tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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